The use of pesticides in agriculture in North-East Benin

Int. J. Agron. Agri. R.
International Journal of Agronomy and Agricultural Research (IJAAR)
ISSN: 2223-7054 (Print) 2225-3610 (Online)
http://www.innspub.net
Vol. 12, No. 6, p. 48-63, 2018
RESEARCH PAPER
OPEN ACCESS
The use of pesticides in agriculture in North-East Benin
Sèmanou Robert Dognon *1,2 , Hindé Reine Dognon 3 , Alassane Youssao Abdou Karim 4 ,
Marie-Louise Scippo 2 , Issaka Youssao Abdou Karim 1
1
Department of Animal Production and Health, Laboratory of Animal Biotechnology and Meat Technology,
Polytechnic School of Abomey-Calavi, University of Abomey-Calavi, Cotonou, Benin
2
Department of Food Sciences, Laboratory of Food Analysis, Faculty of Veterinary Medicine,
FARAH-Veterinary Public Health, University of Liège, bât. B43bis, 10 Avenue de Cureghem,
Sart-Tilman, Liège, Belgium
3
Department of Hydrology and Management of Water Resources, National Institute of Water,
Laboratory of Applied Hydrology, University of Abomey-Calavi, Cotonou, Benin
4
Unit of Research in Ecotoxicology and Study Quality, Laboratory of Research in Applied Chemistry,
Polytechnic School of Abomey-Calavi, Cotonou, Benin
Article published on June 30, 2018
Key words: Pesticide, Agriculture, Survey, Benin
Abstract
In Benin, agriculture sector contributes to 26 % of the Gross Domestic Product in 2016. Intensification of
agriculture has led to increase the use of new pesticides. This paper aims to asses practice attitudes of chemical
pesticide use by farmers in North-East of Benin. Semi-structured interviews were conducted among 100 farmers
chosen by the ‘‘snowball sampling’’ process in the communes of Banikoara, Kandi, Bembereke and Kalale. Survey
was focused on farmer status, farming crops, pesticides and their use in agriculture. Descriptive statistics were
performed with MS Excel while SAS software was used to perform chi-2 tests. Multiple correspondence analysis
and hierarchical classification analysis were conducted to establish a farmer typology. Ninety-one percent of
farmers were men and the predominant ethnic groups (p

Int. J. Agron. Agri. R.
Introduction
In Benin, agriculture is the second sector (after
services) that contributes to 26 % of the Gross
Domestic Product in 2016 (The World Bank, 2016;
Unpublished). Several vegetable crops as cotton are
cultivated in this country. Cotton is the main groundrent
crop in the country (Lau et al., 2015). There is a
strong link between cotton prices and rural welfare in
Benin (Minot and Daniels, 2005). So, cotton culture
is very important for this country development.
However, cotton plant is very sensitive to several
diseases from arthropod ravagers, of which
Helicoverpa armigera causes mostly nuisances
(Alavo et al., 2010). Among all the existing methods
of fight, chemical control is the most dominant with
spraying of very important annual quantities of
various pesticides (Agbohessi et al., 2011). In
addition, in relation to rapid human population
increase, intensification of agriculture to satisfy food
needs and to contribute to global hunger reduction
has led to increasing use of new pesticides (Pretty et
al., 2011; Tscharntke et al., 2012; Sarwar and Salman,
2015).
Pesticides are very important for food crops
production and for human health. These products are
used to prevent and/or control undesirable organisms
in agriculture or public health (Damalas et al., 2011;
Sarwar and Salman, 2015).
Several studies proved that pesticide use involves
presence of their residues in environment and caused
many nuisances living beings because of pesticide
toxicity to animals and human health (Dawson et al.,
2010 ; Damalas et al., 2011; Gbaguidi et al., 2011;
Mesnage et al., 2014; Aïkpo et al., 2016). Others
authors found pesticide residues in foods from
vegetables and animals (Krüger et al., 2014; Shehata
et al., 2014; Ehling and Reddy, 2015; Ezemonye et al.,
2015).
Nevertheless, most of farmers living in some African
rural areas used pesticides following bad attitude
practices (Agbohessi et al., 2011; Hinson et al., 2015;
Negatu et al., 2016). Thus, it was necessary that this
study assessed practice attitudes of chemical
pesticides by agricultural farmers in North-East of
Benin. Different pesticides used in the study area had
been inventoried.
Materials and methods
Study area
In this study, data were collected in the
departments of Borgou and Alibori in the North-
East of Benin (Fig. 1).
Fig. 1. Location of surveyed farmers in the departments of Borgou and Alibori.
Dognon et al. Page 49

Int. J. Agron. Agri. R.
The department of Alibori is located in the extreme
North of Benin. It is bounded on the East by the
Federal Republic of Nigeria, to the North by the
republics of Burkina-Faso and Niger, to the South by
the department of Borgou and to the West by the
department of Atacora.
It has an area of 26,242 km² (23 % of national
territory) and a population estimated at 867,463
inhabitants (8.7 % of the national population) in 2013
(INSAE, 2015, Unpublished).
The climate is Sudano-Sahelian and the annual
rainfall varies between 700 and 1200 mm (Adam et
Boko, 1993).
The Department of Borgou covers 25,856 km². It is
bounded to the North by the department of Alibori,
South by the departments of Collines and Donga, to
the East by the Federal Republic of Nigeria, and to the
West by the department of Atacora.
Its population was estimated at 1,214,249 inhabitants
(12.1% of the national population) in 2013 (INSAE,
2015; Unpublished). The climate is Sudanese type and
the annual rainfall varies from 900 to 1300 mm
(Adam et Boko, 1993).
In the two departments, the main activity of the
populations is agriculture that is family-type.
Livestock is very developed with large herds of cattle,
sheep, goats, poultry, etc.
Interviews
Choice of the survey communes
Four communes were chosen following two steps.
Firstly, according to data available in activity reports
of the Regional Agricultural Centre for Rural
Development of the departments of Borgou and
Alibori, the first three communes of cotton
production in 2014-2015 were pre-selected by
department. Among these preselected communes, the
first two,which had more bovine herd were chosen in
each department. In fine, the communes of Banikoara
and Kandi in the department of Alibori ; Bembereke
and Kalale in Borgou were selected (Fig. 1).
Choice of the surveyed farmers
Farmers were selected according to the ''snowball
sampling '' process. The first respondents were
chosen with the help of the service of Communal
Sector for Agricultural Development in each selected
commune. Four Specialized Technicians of Vegetable
Production (TSPV) wereinterviewed as key
informants. The first respondents helpt to choose the
followsurveyed farmers. The availability of
respondents to provide the maximum of information
was a key factor in their choice. A total of 100 farmers
were interviewed (25 farmers by commune).
Data collection
From September to November 2015, semi-structured
interviews were conducted in face to face with each of
the selected farmers. With the help of a translator,
interviews were conducted in local languages on the
topics relating to the farmer status, growncrops,
pesticides used and attitude practices of farmer to
apply pesticides. Some informations such as
cultivated superficie of each crop, pesticide used on
vegetable andpesticide sources were systematically
recorded to be analyzed quantitatively.
Data analysis
Data from semi-sructured survey were translated in a
structured form in MS Excel 2013 to perform
descriptive statistics. SAS software (version 9) was
used to perform chi-2 test for the statistical
comparisons between proportions of each modality.
In the data base, surveyed farmers were numbered
from 1 to 100 following the interview rank. Banikoara
farmers were numbered from 1 to 25, those of Kandi
26 to 50, those of Bembèrèkè 51 to 75 and those of
Kalalé 76-100.
R software version 3.2.5 (package Facto Mine R,
functions MCA and HCPC) was used as described by
Husson et al. (2017) to perform multiple
correspondence analysis (MCA) and hierarchical
classification analysis (HCA) of variables related to
risk practices in the use of pesticides by farmers. MCA
is a statistical method to analyze the pattern of
relationships between a set of qualitative variables.
Dognon et al. Page 50

Int. J. Agron. Agri. R.
This method projects the variable modalities on maps
based on their correlations. As for the HCA, it based
on the MCA results in order to project each of the
farmers according to their modalities on an area map
defined by virtual axis.
This algorithmic approach allowed defining
hierarchically discrete groups (clusters) following the
branches of a dendrogram. MCA and HCA results
were interpreted as described by Dognon et al.
(2018).
In this study, 9 variables with 26 modalities were
used (Table 1) in order to establish a typology of the
farmers according to their practices. Statistical
comparisons were performed between farmer
proportions (p) relative to different modalities of the
clusters by determining confidence intervals (CIs) at
95 % using the formula
(n=total number in the sampled population). Each
farmer cluster was then classified according to the
risk-group using an assessment grid of practices able
to cause contamination of environment and food
products by pesticide residues (Table 2 adapted from
Dognon et al. (2018)).
This assessment grid was based on modalities linked
to good practices of pesticide use. In relation to each
of these modalities, the risk was considered as high,
medium and low if proportion of associate farmers
was lower than 20 %, between 20 % and 50 %, and
above 50 % of the total cluster farmers, respectively
(Table 2). The risk level of each cluster was taken as
the highest rate presented by the cluster across the
grid.
Results
Farmer status
Table 3 shows the proportions of farmers in relation
to their status modalities and the results of
significance tests between the final proportions and
between farmer percentages following sampled
commune. Ninety-one percent of farmers were men.
Seventy-four percent of the responding had age
ranging from 30 to 59 old years.
Table 1. Codes of modalities used for the MCA and the HCA.
Variables Modalities Codes
Commune Bembèrèkè bke
Kandi
kdi
Banilkoara
bnk
Kalalé
kal
Instruction level No instruction inst0
Primary school
inst1
Secondary school
inst2
Local language literate No lang0
Yes
lang1
Knowledge of use modal of the pesticides No know0
Yes
know1
Yes sometimes
know2
Technician helping No tech0
Yes
tech1
Yes sometimes
tech2
Purchase site of the pesticides Market sourceM
SCDA
sourceS
SCDA and market
sourceT
Management of empty bottles No disposal precaution bottle0
Burnt
bottle1
Recycled for sell
bottle2
Buried
bottle3
Recycled for use
bottle4
Pasturing of crop residues by own bovines No past0
Yes
past1
Pasturing of crop residues by other bovines Yes past
Dognon et al. Page 51

Int. J. Agron. Agri. R.
The predominant ethnic groups were Bariba (31 %),
Gando (29 %) and Mokole (23 %).
Farmers were mostly uneducated (68 %) (p

Int. J. Agron. Agri. R.
Table 3. Status of farmers.
Variables and modalities Proportion of farmers (%) Distribution in communes (%)
Bnk Kdi Bke Kal
Sex
Male 91 a 24.18 e 27.47 e 23.08 e 25.27 e
Female 9 b 33.33 e 0.00 f 44.44 e 22.22 ef
Ages (years)
Age < 30 9 b 22.22 e 33.33 e 22.22 e 22.22 e
30 ≤ Age ≤ 59 74 a 24.32 e 28.38 e 25.68 e 21.62 e
Age ≥ 60 17 b 29.41 ef 5.88 f 23.53 ef 41.18 e
Ethnic groups
Bariba 31 a 61.29 e 3.23 h 35.48 f 0.00 h
Gando 29 a 0.00 h 0.00 h 24.14 f 75.86 e
Mokole 23 a 0.00 f 100.00 e 0.00 f 0.00 f
Fulani 8 b 75.00 e 0.00 f 12.50 f 12.50 f
Djerma 7 bc 0.00 f 14.29 f 71.43 e 14.29 f
Boo 1 c 0.00 e 0.00 e 0.00 e 100.00 e
Nago 1 c 0.00 e 0.00 e 100.00 e 0.00 e
Instruction level
Uneducated 68 a 32.35 e 17.65 f 22.06 ef 27.94 ef
Primary school 15 b 13.33 f 46.67 e 33.33 ef 6.67 f
Secondary school 17 b 5.88 f 35.29 e 29.41 ef 29.41 ef
Local language literate
No 65 a 29.23 e 29.23 e 27.69 e 13.85 f
Yes 35 b 17.14 f 17.14 f 20.00 f 45.71 e
Religion
Islam 71 a 23.94 e 29.58 e 25.35 e 21.13 e
Christianity 26 b 23.08 e 15.38 e 23.08 e 38.46 e
Non-religious 3 c 66.67 e 0.00 e 33.33 e 0.00 e
Principal activities
Agriculture 85 a 21.18 e 24.71 e 25.88 e 28.24 e
Breeding 8 b 75.00 e 0.00 f 12.50 f 12.50 f
Trade 7 b 14.29 ef 57.14 e 28.57 ef 0.00 f
Secondary activities
Breeding 65 a 20.00 e 26.15 e 30.77 e 23.08 e
Agriculture 15 b 46.67 e 26.67 ef 20.00 ef 6.67 f
Trade 20 b 25.00 ef 20.00 ef 10.00 f 45.00 e
Bnk : Banikoara ; Kdi : Kandi ; Bke : Bembereke ; Kal : Kalale ; Letters a, b and c were used to notify significant
difference between proportions of modalities for the same variable; Letters e, f and h were used to notify
significant difference between proportions of the same modality in different communes.
These last proportions were significantly different to
the percentages of farmers using others weedkillers
(p

Int. J. Agron. Agri. R.
Table 4. Proportions of farmers using pesticides following crops in the sampled communes.
Variables and modalities
Proportion of
farmers (%)
Distribution in communes (%)
Bnk Kdi Bke Kal
Maize
Herbicide 96 a 26.04 e 26.04 e 22.92 e 25.00 e
No use of pesticide 4 b 0.00 f 0.00 f 75.00 e 25.00 f
Cotton
Herbicide and Insecticide 94 a 26.60 e 26.60 e 21.28 e 25.53 e
No use of pesticide 6 b 0.00 f 0.00 f 83.33 e 16.67 f
Sorghum
Herbicide 77 a 27.27 e 25.97 e 20.78 e 25.97 e
No use of pesticide 23 b 17.39 e 21.74 e 39.13 e 21.74 e
Leguminous
Herbicide and Insecticide 26 b 53.85 e 15.38 fh 3.85 h 26.92 f
Insecticide 41 a 0.00 h 46.34 e 34.15 ef 19.51 f
No use of pesticide 33 ab 33.33 e 6.06 f 30.30 e 30.30 e
Gardening
Herbicide and Insecticide 15 b 20.00 ef 33.33 e 0.00 f 46.67 e
Insecticide 14 b 7.14 f 21.43 ef 28.57 ef 42.86 e
No use of pesticide 71 a 29.58 e 23.94 e 29.58 e 16.90 e
Rice
Herbicide 22 b 22.73 e 31.82 e 18.18 e 27.27 e
No use of pesticide 78 a 25.64 e 23.08 e 26.92 e 24.36 e
Roots and tubers
Herbicide 17 b 23.53 e 0.00 f 47.06 e 29.41 e
No use of pesticide 83 a 25.30 e 30.12 e 20.48 e 24.10 e
Bnk : Banikoara ; Kdi : Kandi ; Bke : Bembereke ; Kal : Kalale ; Letters a and b were used to notify significant difference between
modality proportions for the same variable; Letters e, f and h were used to notify significant difference between proportions of
the same modality in different communes.
Table 5. Herbicides used by responding farmers in 2015.
N° Trade names Active ingredients Farmer frequency±CI(%)
1 Cottonex PG 560SC Fluometuron+ Prometrin+ Glyphosate 55±9.75 a
2 Nicomaïs Nicosulfuron 47±9.78 a
3 Spring PFG 560SC** Fluometuron+ Prometrin+ Glyphosate 29±8.89 b
4 Kalach 360SL Glyphosate 28±8.80 b
5 Spider** Fluometuron+ Prometrin+ Glyphosate 28±8.80 b
6 Glyphos 360 SL Glyphosate 26±8.60 b
7 Glyphospring 410SL** Glyphosate 26±8.60 b
8 Glycel 41% Glyphosate 18±7.53 bc
9 JAF PFG** Fluometuron+ Prometrin+ Glyphosate 13±6.59 c
10 Atra force* Atrazine# 8±5.32 c
11 Buster* Butachlor 1±1.95 d
12 Buta force* Butachlor 1±1.95 d
13 Callifor G Fluometuron+ Prometrin+ Glyphosate 1±1.95 d
14 Force Up** Glyphosate 1±1.95 d
15 GALLANT Super Haloxyfop methyl ester 1±1.95 d
16 Garill Triclopyr+ Propanil 1±1.95 d
17 Glyphogan 360SL Glyphosate 1±1.95 d
18 Lagon 380 SC Isoxaflutole+ Aclonifene 1±1.95 d
19 ITABARKA** Prometrin 1±1.95 d
20 Terbulor Metolachlor+ Terburnin 1±1.95 d
21 TRIPRO Triclopyr+ Propanil 1±1.95 d
Farmer frequency (%): proportion of farmer using product. CI: Confident interval at 95 %. Letters from a to d were used to
notify significant difference between the percentages. *: Product thattrade name and actives ingredients were not found on the
list of authorized pesticide by the National Comity of Consent and Control of Phytopharmaceutical Products (CNAC) in Benin
(CNAC, 2014; unpublished). **: Product that trade name was not found on the list of authorized pesticide of CNAC (2014;
unpublished) while actives agents were found on that list.
Practices of use of pesticides in the area study
All surveyed farmers used pesticides for control and
prevention of harmful organisms.
Table 7 shows practices of use of pesticides. Seventyeight
percent of farmers purchased pesticides in both
authorized shop of communal sector for agricultural
developpment (SCDA) and illegal market while 17 %
and 5 % of them shoped from SCDA only and illegal
market only, respectively.
Dognon et al. Page 54

Int. J. Agron. Agri. R.
Table 6. Insecticides used by the responding farmers in 2015.
N° Trade names Active ingredients Farmer frequency ± CI (%)
1 Ema star 112 EC Emamectine+ Acetamipride 89±6.13 a
2 Acer 35EC** λ-Cyhalothrin+Acetamipride 78±8.12 b
3 Cutter 112EC Emamectine+ Acetamipride 47±9.78 c
4 Napeco Metafos** Emamectine+ Acetamipride 33±9.22 cd
5 LAMENET 46EC** λ-Cyhalothrin+Acetamipride 25±8.49 d
6 Caïman B** Emamectine 10±5.88 e
7 Lamber** λ-Cyhalothrin+Acetamipride 10±5.88 e
8 Sharp Shoster** Cypermethrin+Profenofos 10±5.88 e
9 Conquest 88 Cypermethrin+ Acetamipride 7±5.00 ef
10 Chemaprid** Cypermethrin+ Acetamipride 3±3.34 f
11 Karto Super 2.5 EC** λ-Cyhalothrin 3±3.34 f
12 Aceta star** Bifenthrin+ Acetamipride 2±2.74 f
13 Conquest plus 388EC Cypermethrin+ Acetamipride+
2±2.74 f
Triazophos
14 Cyperkill 10EC** Cypermethrin 2±2.74 f
15 EMA SUPER 56 DC Emamectine+ Acetamipride 2±2.74 f
16 Fanga 500EC Profenofos 2±2.74 f
17 KD+** λ-Cyhalothrin+ Chlorpyriphos 2±2.74 f
18 Lampride** λ-Cyhalothrin+ Acetamipride 2±2.74 f
19 Moacartarine** λ-Cyhalothrin+ Acetamipride 2±2.74 f
20 Nurelle D 35/300 Cypermethrin+ Chlorpyriphos
2±2.74 f
ethyl
21 Profenet 500 EC** Profenofos 2±2.74 f
22 Steward 150 EC Indoxacarbe 2±2.74 f
23 Tenor 500 EC** Profenofos 2±2.74 f
24 Thunder 145 o-teq Imidaclopride+ Beta-cyfluthrin 2±2.74 f
25 Tihan 175 o-teq Spirotetramate+ Flubendiamide 2±2.74 f
26 CALIF 500EC Profenofos 1±1.95 f
27 Champion Cyhalon 2.5 EC** λ-Cyhalothrin 1±1.95 f
28 D-BAN Super 480EC** Chlorpyrifos-ethyl 1±1.95 f
29 Emacot 019EC Emamectine 1±1.95 f
30 Emectine 19.2EC** Emamectine 1±1.95 f
31 EMIR 88EC Cypermethrin+ Acetamipride 1±1.95 f
32 FAIZER** Indoxacarbe 1±1.95 f
33 Lamdacal P 315 EC λ-Cyhalothrin+ Profenofos 1±1.95 f
34 Nurelle D 36/200 Cypermethrin+ Chlorpyriphos
1±1.95 f
ethyl
35 Profenofos** Profenofos 1±1.95 f
Farmer frequency (%): proportion of farmer using product; CI: Confident interval at 95 %; Letters from a to f
were used to notify significant difference between percentages. **: Product that trade name was not found on the
list of authorized pesticide by the CNAC (2014, unpublished) in Benin while actives ingredients were found on
that list.
Proportion of farmers who purshased pesticides in
SCDA shop exclusivelly was more in Banikoara
(47.06 %) and Kalale (47.06 %) than Kandi (5.88 %)
and Bembereke (0 %). This last commune had the
highest proportion (80 %) of farmers who solely got
pesticides from illegal market.
The modals of the pesticide use had sometimes been
known by 53 % of farmers and regulary known by
45 % of them. Sixteen percent of the farmers were not
ever assisted by the technicians of vegetable
production.
As for the management of pesticide empty bottles,
‘‘no disposal precaution’’ (60 %) was the dominant
modality (p

Int. J. Agron. Agri. R.
Table 7. Practice of use of pesticides.
Variables and modalities Proportion of farmers (%) Distribution in communes (%)
Bnk Kdi Bke Kal
Purchase site
SCDA and Market 78 a 21.79 e 29.49 e 26.92 e 21.79 e
SCDA 17 b 47.06 e 5.88 f 0.00 f 47.06 e
Market 5 c 0.00 f 20.00 f 80.00 e 0.00 f
Knowledge of use modal
Yes sometimes 53 a 43.40 e 43.40 e 9.43 f 3.77 f
Yes 45 a 4.44 f 4.44 f 44.44 e 46.67 e
No 2 b 0.00 f 0.00 f 0.00 f 100.00 e
Technician helping
Yes sometimes 52 a 46.15 e 44.23 e 9.62 f 0.00 h
Yes 32 b 3.13 f 6.25 f 43.75 e 46.88 e
No 16 c 0.00 f 0.00 f 37.50 e 62.50 e
Management of empty bottles
No disposal precaution 60 a 36.67 e 15.00 f 28.33 ef 20.00 f
Burnt 17 b 0.00 f 17.65 f 29.41 e 52.94 e
Recycled for sell 12 bc 0.00 f 83.33 e 16.67 f 0.00 f
Buried 6 c 0.00 f 16.67 f 16.67 f 66.67 e
Recycled for use 5 c 60.00 e 40.00 ef 0.00 f 0.00 f
Knowledge of adverse effect of pesticides
No effect 42 a 45.24 e 23.81 f 23.81 f 7.14 h
Human diseases 24 b 25.00 f 0.00 h 0.00 h 75.00 e
Animal diseases 18 b 0.00 f 44.44 e 55.56 e 0.00 f
Animal and human diseases 16 b 0.00 f 43.75 e 31.25 e 25.00 e
Bovine herd holder
Yes 85 a 29.41 e 27.06 e 14.12 f 29.41 e
No 15 b 0.00 f 13.33 f 86.67 e 0.00 e
Pasturing of crop residues by own bovines
Yes 84 a 29.76 e 27.38 e 13.10 f 29.76 e
No 16 b 0.00 f 12.50 f 87.50 e 0.00 f
Pasturing of crop residues by other bovines
Yes 100 25.00 e 25.00 e 25.00 e 25.00 e
Bnk : Banikoara ; Kdi : Kandi ; Bke : Bembereke ; Kal : Kalale ; Letters a, b and c were used to notify significant
difference between proportions of modalities for the same variable; Letters e, f and h were used to notify
significant difference between proportions of the same modality in different communes.
Many farmers (42 %) declared that they did not know
any adverse effect caused by pesticides on animal and
human beings.
As for the human diseases, farmers declared that
pesticides made themself fell dizzy. Eighty-five
percent of farmers were bovine herd holders.
This proportion is significantly upper (p

Int. J. Agron. Agri. R.
Table 8. Distribution of farmers in the three classes according to the whole modalities.
Variables/Modalities Farmer number (n=100) Proportions of farmer distribution in the clusters (% ± CI)
Cluster 1 Cluster 2 Cluster 3
Commune
Bembèrèkè 25 0.0 ± 0.0 b 100.0 ± 0.0 a 6.9 ± 9.2 b
Kandi 25 50.0 ± 14.1 a 0.0 ± 0.0 b 3.4 ± 6.6 b
Banikoara 25 50.0 ± 14.1 a 0.0 ± 0.0 b 3.4 ± 6.6 b
Kalalé 25 0.0 ± 0.0 b 0.0 ± 0.0 b 86.2 ± 12.6 a
Instruction level
No instruction 68 70.8 ± 12.9 a 56.5 ± 20.3 a 72.4 ± 16.3 a
Primary school 15 16.7 ± 10.5 a 21.7 ± 16.9 a 6.9 ± 9.2 a
Secondary school 17 12.5 ± 9.4 a 21.7 ± 16.9 a 20.7 ± 14.7 a
Local language literate
No 65 79.2 ± 11.5 a 78.3 ± 16.9 a 31.0 ± 16.8 b
Yes 35 20.8 ± 11.5 b 21.7 ± 16.9 b 69.0 ± 16.8 a
Knowledge of use modal
No 2 0.0 ± 0.0 a 0.0 ± 0.0 a 6.9 ± 9.2 a
Yes 45 4.2 ± 5.7 b 78.3 ± 16.9 a 86.2 ± 12.6 a
Yes sometimes 53 95.8 ± 5.7 a 21.7 ± 16.9 b 6.9 ± 9.2 b
Technician helping
No 16 0.0 ± 0.0 b 21.7 ± 16.9 a 37.9 ± 17.7 a
Yes 32 4.2 ± 5.7 b 56.5 ± 20.3 a 58.6 ± 17.9 a
Yes sometimes 52 95.8 ± 5.7 a 21.7 ± 16.9 b 3.4 ± 6.6 c
Purchase site
Market 5 2.1 ± 4.0 ab 17.4 ± 15.5 a 0.0 ± 0.0 b
SCDA 17 18.8 ± 11.0 a 0.0 ± 0.0 b 27.6 ± 16.3 a
SCDA and market 78 79.2 ± 11.5 a 82.6 ± 15.5 a 72.4 ± 16.3
Management of empty bottles
No disposal
60 62.5 ± 13.7 ab 69.6 ± 18.8 a 48.3 ± 18.2 b
precaution
Burnt 17 4.2 ± 5.7 b 17.4 ± 15.5 b 37.9 ± 17.7 a
Recycled for sell 12 20.8 ± 11.5 a 8.7 ± 11.5 b 0.0 ± 0.0 b
Buried 6 2.1 ± 4.0 a 4.3 ± 8.3 a 13.8 ± 12.6 a
Recycled for use 5 10.4 ± 8.6 a 0.0 ± 0.0 b 0.0 ± 0.0 b
Pasturing of crop residues by own bovines
No 16 4.2 ± 5.7 b 60.9 ± 19.9 a 0.0 ± 0.0 b
Yes 84 95.8 ± 5.7 a 39.1 ± 19.9 b 100.0 ± 0.0 a
Pasturing of crop residues by other bovines
Yes 100 100.0 ± 0.0 a 100.0 ± 0.0 a 100.0 ± 0.0 a
CI: Confidence interval at 95 %; Letters a, b and c were used to notify significant difference between proportions
of modalities about in clusters.
The analysis of the indicator matrix shown
respectively for the axes 1, 2 and 3 a value of 18.69 %,
13.06 % and 9.77 % of the total inertia (Fig. 3 and 4).
The component 1 opposes the districts of Banikoara
and Kandi, associated to modalities of farmers who
did not follow local language literate, who sometimes
know the use modal of the pesticides and get
agricultural technician helps for the pesticide use
(negative coordinates, Fig. 3), to the district of Kalalé,
where some farmers received local language lecture
and were not helped by the agricultural technicians
(positive coordinates, Fig. 3).
The component 2 discriminates the group of
modalities related to farmers in the district of
Bembèrèkè (positive coordinates, Fig. 3) to other
groups of farmers in Banikoara on the one hand and
Kalalé on the other hand (negative coordinates,
Fig. 3).
As for the component 3, it opposes farmers who
purchase pesticides from legal shop (SCDA) to those
who use local market as sources of supply of
pesticides.
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Int. J. Agron. Agri. R.
Hierarchical classification analysis and description
of classes
Fig. 4 shows the projection of individual farmers on
the plane defined by the factorial axes 1 and 2, which
allowed observing the affinity groups in three
clusters, holding 48 % of the total variability between
groups. Table 8 shows the distribution of farmers in
the three classes according to all modalities of which
the most relevant by farmer class are presented in
Table 9.
Table 9. Distribution of farmers in the three classes according to the most relevant modalities (percentage of
modalities compared to the farmer number in each cluster).
Variables/Modalities
Proportions of farmer distribution in the clusters
(% ± CI)
Cluster 1 Cluster 2 Cluster 3
Specific modalities to cluster 1
Commune : Kandi 50.0 ± 14.1 a 0.0 ± 0.0 b 3.4 ± 6.6 b
Commune : Banikoara 50.0 ± 14.1 a 0.0 ± 0.0 b 3.4 ± 6.6 b
Knowledge of use modal : yes sometimes 95.8 ± 5.7 a 21.7 ± 16.9 b 6.9 ± 9.2 b
Technician helping : yes sometimes 95.8 ± 5.7 a 21.7 ± 16.9 b 3.4 ± 6.6 c
Management of empty bottles : recycled for sell 20.8 ± 11.5 a 8.7 ± 11.5 b 0.0 ± 0.0 b
Management of empty bottles : recycled for use 10.4 ± 8.6 a 0.0 ± 0.0 b 0.0 ± 0.0 b
Specific modalities to cluster 2
Commune : Bembèrèkè 0.0 ± 0.0 b 100.0 ± 0.0 a 6.9 ± 9.2 b
Pasturing of crop residues by own bovines : no 4.2 ± 5.7 b 60.9 ± 19.9 a 0.0 ± 0.0 b
Specific modalities to cluster 3
Commune : Kalalé 0.0 ± 0.0 b 0.0 ± 0.0 b 86.2 ± 12.6 a
Local language literate : yes 20.8 ± 11.5 b 21.7 ± 16.9 b 69.0 ± 16.8 a
Management of empty bottles : burnt 4.2 ± 5.7 b 17.4 ± 15.5 b 37.9 ± 17.7 a
Common modalities to at least two clusters
Pasturing of crop residues by other bovines : yes 100.0 ± 0.0 a 100.0 ± 0.0 a 100.0 ± 0.0 a
Purchase site : SCDA and market 79.2 ± 11.5 a 82.6 ± 15.5 a 72.4 ± 16.3 a
Instruction level : no instruction 70.8 ± 12.9 a 56.5 ± 20.3 a 72.4 ± 16.3 a
Local language literate : no 79.2 ± 11.5 a 78.3 ± 16.9 a 31.0 ± 16.8 b
Management of empty bottles : no disposal precaution 62.5 ± 13.7 ab 69.6 ± 18.8 a 48.3 ± 18.2 b
Pasturing of crop residues by own bovines : yes 95.8 ± 5.7 a 39.1 ± 19.9 b 100.0 ± 0.0 a
Knowledge of use modal : yes 4.2 ± 5.7 b 78.3 ± 16.9 a 86.2 ± 12.6 a
Technician helping : yes 4.2 ± 5.7 b 56.5 ± 20.3 a 58.6 ± 17.9 a
CI: Confidence interval at 95 %; Letters a, b and c were used to notify significant difference between proportions
of modalities about in clusters.
Cluster 1 counted 48 farmers (48 ± 14.1 % of total
farmers) from Banikoara and Kandi districts.
They sometimes get helps of agricultural
technician and sometimes know the use modal of
pesticides (Table 9).
Cluster 2 was composed of 23 ± 17.2 % of the farmers
(all from Bembèrèkè) (Table 8). Several farmers of
this group (60.9 ± 19.9 %) were characterized by the
modality of “pasturing of crop residues by own
bovines (no)” (Table 9).
As for the cluster 3, it contained 29 farmers (29 ± 16.5
% of the total farmers which of 25 from Kalalé, 2 from
Bembèrèkè, 1 in Kandi and 1 in Banikoara). Most of
these farmers followed local language literate and
burned the empty bottles of pesticides (Table 9).
The specific modalities of each cluster (Table 9)
crossed with the assessment grid of the risk level of
practices (Table 2), allowed identifying clusters 2 and
3 as the lower-risk classes while cluster 1 was highrisk
class able to cause contamination of environment
and foods from animal bred in the study area by the
pesticide residues.
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Int. J. Agron. Agri. R.
Discussion
Status of the farmers
Surveyed farmers were dominated by men (91 %). The
same observation was done by Hinson et al. (2015) in
Banikoara where responding farmer sample
contained 94.8 % of men. These last authors showed
that farmers were majoritary uneducated (80.3 %).
That seemed to be similar to the whole of instruction
level in this study (68 % of farmers were unaducated).
So, few farmers attended primary and secondary school.
In addition, only 35 % of them were done local language
literate. Thus, informations on pesticide labels would not
be understood by majoritary of the farmers. This
situation could favour the bad use of pesticides.
Fig. 2. Average proportions of farmers following crops cultivated from 2006 to 2015. Error bars represent
standard deviations (SD) while letters from a to h were used to notify significant difference between percentages
(p

Int. J. Agron. Agri. R.
The same reasons justify the production of sorghum
in the area study where this cereal is mostly used for
their own foods. As for the cotton, this plant is the
main ground-rent crop which contributes strongly to
the country economy (Minot and Daniels, 2005; Lau
et al., 2015). So, cotton crop is not only very
important for the study area economy but also for the
development of the whole of the country.
According to Alavo et al. (2010), cotton plant is very
sensitive to several diseases from arthropod ravagers
as H. armigera. That justifies the use of many
insecticide products on cotton plant by almost of the
farmers (94 %). This level is very upper than the 69 %
of chemical pesticide users surveyed in Banikoara in
2011 (Agbohessi et al., 2011).
Fig. 4. Graphic representation of farmer clusters on the factorial axes 1 and 2. The numbers correspond to the
farmers who are represented on the map by “ ”, “
” and “ ” in the clusters 1, 2 and 3, respectively.
Banikoara is the first commune of cotton production
in Benin. Thus, several of cotton harmful insects
attack also leguminous during growing. This situation
justifies the proportion of the pesticide users on
leguminous crops in Banikoara (53.85 %) which was
significantly higher (p

Int. J. Agron. Agri. R.
But in 2011, Banikoara’s farmers used nine active
chemicls from seven products to kill cotton harmful
insects (Agbohessi et al., 2011). According to Hinson
et al. (2015), acetamipride was present in 13/27 of the
insecticide products, this chemical was also the most
active compounds in 37 % of the insecticide products
documented during this survey.
Practices of pesticide use by the farmers
Contrary to Agbohessi et al. (2011) results, which
showed that 67 % of farmers purchased pesticides
from formal sector in Banikoara, this study revealed
that only 17 % of surveyed farmers got pesticide from
authorized shop. Thus, 83 % of the farmers purchased
pesticides in both illegal markets and/or in
authorized shop. According to Agbohessi et al. (2011),
any control was performed about pesticides sold in
informal sector from cross-border trading (Togo,
Nigeria, Ghana, etc.). These products could be store
in bad conditions and are more cheaper than official
pesticides. Thus, farmers accessed easily to these
prohibited and smuggled products. This situation
justify the presence of some pesticides that active
ingredient was not authorized. For example, Atra
force, Buster and Buta force contained Atrazine and
Butachlor that were not authorized to be used in
Benin (CNAC, 2014, Unpublished). This Beninese
situation is similar to that of Ethiopia where direct
import of pesticides outside of legal chain was
observed (Negatu et al., 2016).
Despite of the banning of endosulfan use in Benin
since 2007 (Badarou and Coppieters, 2009), this
pesticide was using in Banikoara three years after the
deadline (December 2008) (Agbohessi et al., 2011).
Fortunately, this study did not show any endosulfan
product. This happy situation was not similar to that
of Ethiopia where illegitimate usages of DDT and
endosulfan on food crops continued until 2016
(Negatu et al., 2016).
Moreover, the difference between percentages of
farmers helped by the technician from the different
communes could be justified by an unequal
distribution of the technicians in these municipalities.
Farmer group according to risky practices
The typology of the farmers in North-East Benin as
established in this study allowed characterizing two
categories of farmers. Cluster 1 was the high-risk class
and mostly composed of farmers from Banikoara and
Kandi districts in the Department of Alibori, while the
lower-risk classes (clusters 2 and 3) were mostly
composed of farmers from Bembèrèkè and Kalalé
districts in the Department of Borgou. Thus, the
Departments were strongly liked to the risky practices
of the use of the pesticide products. In fact, Alibori is
the first Department of cotton production in Benin.
The limited number of the agricultural technician in
this locality did not allow covering the most of these
cotton farmers who largely used pesticides from
several illegal sources. However, concerning the
common modalities (Table 9), it is important to recall
that none of the three farmer classes displays the
minimum conditions for ensuring a healthy
environment for food safety.
All these observations support the hypothesis that the
pesticide sector in Benin should be reorganized with
enhanced measures of control and follow
recommended practices. Based on these observations,
it is necessary to study the prevalence of pesticide
residues in foods in generally and particularly in
foods from livestock animals. Finally, the
reinforcement of the legislation on pesticide practices
is essential for the well-being of animals and animal
food consumers.
Acknowledgements
The authors are grateful to farmers, rural
development officers of Communal Sectors for the
Agricultural Development of the study area and the
translators for their respective interests in the
investigation.
Competing interests
The authors declare that they have no financial or
personal relationships that may have inappropriately
influenced them in writing of this article.
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